Quantum computing has recently emerged as a transformative technology. Yet, its promised advantages rely on efficiently translating quantum operations into viable physical realizations. In this work, we use generative machine learning models, specifically denoising diffusion models (DMs), to facilitate this transformation. Leveraging text-conditioning, we steer the model to produce desired quantum operations within gate-based quantum circuits. Notably, DMs allow to sidestep during training the exponential overhead inherent in the classical simulation of quantum dynamics -- a consistent bottleneck in preceding ML techniques. We demonstrate the model's capabilities across two tasks: entanglement generation and unitary compilation. The model excels at generating new circuits and supports typical DM extensions such as masking and editing to, for instance, align the circuit generation to the constraints of the targeted quantum device. Given their flexibility and generalization abilities, we envision DMs as pivotal in quantum circuit synthesis, enhancing both practical applications but also insights into theoretical quantum computation.

翻译：量子计算近期作为一种变革性技术崭露头角。然而，其承诺的优势依赖于将量子操作高效转化为可行的物理实现。在本研究中，我们利用生成式机器学习模型——特别是去噪扩散模型——来促进这一转化过程。通过文本条件控制，我们引导模型在基于门的量子电路中生成所需的量子操作。值得注意的是，扩散模型在训练过程中能够规避量子动力学经典模拟中固有的指数级开销——而这正是以往机器学习技术中持续存在的瓶颈。我们通过两项任务展示了该模型的能力：纠缠生成与酉编译。该模型在生成新型电路方面表现出色，并支持扩散模型的典型扩展功能（如掩码和编辑），从而可将电路生成与目标量子设备的约束条件对齐。鉴于其灵活性与泛化能力，我们预见扩散模型将在量子电路合成中发挥关键作用，不仅增强实际应用，同时深化对理论量子计算的理解。